MSFF-Net: Multi-Stream Feature Fusion Network for surface electromyography gesture recognition.

In the field of surface electromyography (sEMG) gesture recognition, how to improve recognition accuracy has been a research hotspot. The rapid development of deep learning provides a new solution to this problem. At present, the main applications of deep learning for sEMG gesture feature extraction are based on convolutional neural network (CNN) structures to capture spatial morphological information of the multichannel sEMG or based on long short-term memory network (LSTM) to extract time-dependent information of the single-channel sEMG. However, there are few methods to comprehensively consider the distribution area of the sEMG signal acquisition electrode sensor and the arrangement of the sEMG signal morphological features and electrode spatial features. In this paper, a novel multi-stream feature fusion network (MSFF-Net) model is proposed for sEMG gesture recognition. The model adopts a divide-and-conquer strategy to learn the relationship between different muscle regions and specific gestures. Firstly, a multi-stream convolutional neural network (Multi-stream CNN) and a convolutional block attention module integrated with a resblock (ResCBAM) are used to extract multi-dimensional spatial features from signal morphology, electrode space, and feature map space. Then the learned multi-view depth features are fused by a view aggregation network consisting of an early fusion network and a late fusion network. The results of all subjects and gesture movement validation experiments in the sEMG signal acquired from 12 sensors provided by NinaPro's DB2 and DB4 sub-databases show that the proposed model in this paper has better performance in terms of gesture recognition accuracy compared with the existing models.

The construction of Chinese microblog gender-specific thesauruses and user gender classification.

Based on the statistical features, short text messages published by different gender users are different in terms of the words and semantics used. In this paper, two new features are constructed after constructing a gender-specific thesaurus. A new classification model is constructed by combining the traditional statistical features and the improved text implicitness feature. The experimental evaluation performed on the Sina Weibo dataset demonstrated the effectiveness of gender-specific thesaurus-based features, and the improved text implicitness feature improved the accuracy of gender classification to 84.7%.